4.7 Article

High sulfur dioxide deposition velocities measured with the flux-gradienttechnique in a boreal forest in the Alberta Oil Sands Region

Journal

ATMOSPHERIC CHEMISTRY AND PHYSICS
Volume 23, Issue 13, Pages 7241-7255

Publisher

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/acp-23-7241-2023

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The emission of SO2 from the Athabasca Oil Sands Region (AOSR) significantly affects the surrounding forest area. Measurements using the flux-gradient method at two tower sites downwind of AOSR SO2 emissions show that the deposition rate of SO2 to the forest is much higher than previously estimated by models. The measured deposition velocities (2.1-5.9 cm s(-1)) are approximately double the previous aircraft-based measurements and more than 10 times higher than model estimates, indicating a shorter atmospheric lifetime for SO2 in the AOSR.
The emission of SO2 from the Athabasca Oil Sands Region (AOSR) has been shown to impact the surrounding forest area. Recent studies using aircraft-based measurements have demonstrated that deposition of SO2 to the forest is at a rate many times higher than model estimates. Here we use the flux-gradient method to estimate SO2 deposition rates at two tower sites in the boreal forest downwind of AOSR SO2 emissions. We use both continuous and passive sampler measurements and compare the two techniques. The measurements infer SO2 deposition velocities ranging from 2.1-5.9 cm s(-1) (when corrections are applied). There are uncertainties associated with the passive sampler flux-gradient analysis, primarily due to an assumed Schmidt number, a required assumption of independent variables, and potential wind effects. We estimate the total uncertainty as & PLUSMN; 2 cm s(-1). Accounting for these uncertainties, the range of measurements is approximately double the previous aircraft-based measurements (1.2-3.4 cm s(-1)) and more than 10 times higher than model estimates for the same measurement periods (0.1-0.6 cm s(-1)), suggesting that SO2 in the AOSR has a much shorter lifetime in the atmosphere than is currently predicted by models.

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